User profiles have an important role in multi-agent recommender systems. The information stored in them improves the system's generated recommendations. Multi-agent recommender systems learn from previous recommendations to update users' profiles and improving next recommendations according to the user feedback. However, when the user does not evaluate the recommendations the system may deliver poor recommendations in the future. This paper presents a mechanism that explores user information from social networks to update the user profile and to generate implicit evaluations on behalf of the user. The mechanism was validated with travel packages recommendations and some preliminary results illustrate how user information gathered from social networks may help to improve recommendations in multi-agent recommender systems.

This paper shows that the combination of knowledge and corpus-based word-to-word similarity measures can produce higher agreement with human judgment than any of the in-dividual measures. While this might be a predictable result, the paper provides insights about the circumstances under which a combination is productive and about the improve-ment levels that are to be expected. The experiments presented here were conducted using the word-to-word similarity measures included in SEMILAR, a freely available semantic similarity toolkit.

Healthcare informatics focuses on the efficient and effective acquisition, management, and use of information in healthcare.

This paper presents a study where semantic frames are used to mine financial news so as to quantify the impact of news on the stock market. We represent news documents in a novel semantic tree structure and use tree kernel support vector machines to predict the change of stock price. We achieve an efficient computation through linearization of tree kernels. In addition to two binary classification tasks, we rank news items according to their probability to affect change of price using two ranking methods that require vector space features. We evaluate our rankings based on receiver operating characteristic curves and analyze the predictive power of our semantic features. For both learning tasks, the proposed semantic features provide superior results.

In one hand, the meaning of natural languages is often described with basic semantic features and a boolean composition of these features. However, this approach is not sufficient to describe more deeply the meaning of linguistic units. In the other hand, the semantic of computer languages often starts from Church’s λ-calculus and walks up to more abstract levels. In this communication, is introduced a new general computational approach of the representation of meanings for high level languages (natural languages and programming languages), in working from the paradigm of compilation in computer sciences. In this compilation paradigm, the expressions of a high level symbolic language are changed in representations by means of intermediary levels, to hit formal representations directly compatible with the material structures of a machine or of a brain. From this analogy, expressions of a natural language can be analyzed by different metalinguistic levels of representations linked to each other by changing representation processes. The communication will give examples of representation changing between expressions of English and representations of meanings expressed by algebra of “treilles”.

Past research on grammar induction has found promising results in predicting parts-of-speech from n-grams using a fixed vocabulary and a fixed context. In this study, we investigated grammar induction whereby we varied vocabulary size and context size. Results indicated that as context increased for a fixed vocabulary, overall accuracy initially increased but then leveled off. Importantly, this increase in accuracy did not occur at the same rate across all syntactic categories. We also address the dynamic relation between context and vocabulary in terms of grammar induction in an unsupervised methodology. We formulate a model that represents a relationship between vocabulary and context for grammar induction. Our results concur with what has been called the word spurt phenomenon in the child language acquisition literature.

Class imbalance is a frequent problem found in bioinformatics datasets. Unfortunately, the minority class is usually also the class of interest. One of the methods to improve this situation is data sampling. There are a number of dierent data sampling methods, each with their own strengths and weaknesses, which makes choosing one a dicult prospect. In our work we compare three data sampling techniques (Random Undersampling, Random Oversampling, and SMOTE) on six bioinformatics datasets with varying levels of class imbalance. Additionally, we apply two dierent classiers to the problem (5-NN and SVM), and use feature selection to reduce our datasets to 25 features prior to applying sampling. Our results show that there is very little dierence between the data sampling techniques, although Random Undersampling is the most frequent top performing data sampling technique for both of our classiers. We also performed statistical analysis which conrms that there is no statistical dierence between the techniques. Therefore, our recommendation is to use Random Undersampling when choosing a data sampling technique, because it is less computationally expensive to implement than SMOTE and it also reduces the size of the dataset, which will improve subsequent computational costs without sacricing classication performance.

In this paper, we address the problem of mining named entity translation such as names of persons, organizations, and locations, from non parallel corpora. First, our study concentrates of different forms of named entity translation. Then, we introduce a new framework to extract all named entity translation types from a non parallel corpus. The proposed framework combines surface and linguistic-based approaches. It is language independent and do not rely on any external parallel resources such as bilingual lexicons or parallel corpora. Evaluations show that our approach for mining named entity translations from a non parallel corpus is highly effective and consistently improves the translation quality of Arabic to French machine translation system.

List of officers of the Florida AI Research Society.

High dimensionality (having a large number of independent attributes) is a major problem for bioinformatics datasets such as gene microarray datasets. Feature selection algorithms are necessary to remove the irrelevant (not useful) and redundant (contain duplicate information) features. One approach to handle this problem is wrapper-based subset evaluation, which builds classification models on different feature subsets to discover which performs best. Although the computational complexity of this technique has led to it being rarely used for bioinformatics, its ability to find the features which give the best model make it important in this domain. However, when using wrapper-based feature selection, it is not obvious whether the learner used within the wrapper should match the learner used for building the final classification model. Furthermore, this question may depend on other properties of the dataset, such as difficulty of learning (general performance without feature selection) and dataset balance (ratio of minority and majority instances). To study this, we use nine datasets with varying levels of difficulty and balance. We find that across all datasets, the best strategy is to use one learner (Na¨ıve Bayes) inside the wrapper regardless of the learner which will be used outside. However, when broken down by difficulty and balance levels, our results show that the more balanced and less difficult datasets work best when the learners inside and outside the wrapper match. Thus, the answer to this question will depend on properties of the dataset.